Eumycetoma is a subcutaneous implantation mycosis often found in the foot. One of the hallmarks of eumycetoma is the formation of grains. These grains are either black or white, and the consistency and morphology differs per causative agent. The two most common causative agents of black-grain eumycetoma are Madurella mycetomatis and Falciformispora senegalensis. Since grains cannot be formed in vitro, in vivo models are needed to study grain formation. Here, we used the invertebrate Galleria mellonella to establish an in vivo grain model for F. senegalensis. Three different F. senegalensis strains were selected, and four different inocula were used to infect G. mellonella larvae, ranging from 0.04 mg/larvae to 10 mg/larvae. Larval survival was monitored for 10 days. Grain formation was studied macroscopically and histologically. The efficacy of antifungal therapy was determined for itraconazole, amphotericin B, and terbinafine. A concentration of 10 mg F. senegalensis per larva was lethal for the majority of the larvae within 10 days. At this inoculum, grains were formed within 24 h after infection. The grains produced in the larvae resembled those formed in human patients. Amphotericin B given at 1 mg/kg 4 h, 28 h, and 52 h after infection prolonged larval survival. No enhanced survival was noted for itraconazole or terbinafine. In conclusion, we developed a F. senegalensis grain model in G. mellonella larvae in which grains were formed that were similar to those formed in patients. This model can be used to monitor grain formation over time and study antifungal efficacy.
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http://dx.doi.org/10.1093/mmy/myad070 | DOI Listing |
Sensors (Basel)
December 2024
School of Mechanical Engineering, Hebei University of Technology, Tianjin 300401, China.
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View Article and Find Full Text PDFSensors (Basel)
December 2024
School of Artificial Intelligence and Big Data, Henan University of Technology, Zhengzhou 450001, China.
In intelligent transportation systems, accurate vehicle target recognition within road scenarios is crucial for achieving intelligent traffic management. Addressing the challenges posed by complex environments and severe vehicle occlusion in such scenarios, this paper proposes a novel vehicle-detection method, YOLO-BOS. First, to bolster the feature-extraction capabilities of the backbone network, we propose a novel Bi-level Routing Spatial Attention (BRSA) mechanism, which selectively filters features based on task requirements and adjusts the importance of spatial locations to more accurately enhance relevant features.
View Article and Find Full Text PDFSensors (Basel)
December 2024
School of Artificial Intelligence and Data Science, Hebei University of Technology, Tianjin 300132, China.
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View Article and Find Full Text PDFNutrients
December 2024
Department of Food Science, Cornell University, Ithaca, NY 14853, USA.
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